View of Field

Distortion: Anatomy of Accurate Imaging (Part 2)

Written by Guest post by Christie Lin | May 7, 2025 5:57:11 PM

Image distortion is a critical determinant of image fidelity and quantifies the geometric accuracy of visualized structures. This parameter represents a fundamental constraint in fluorescence imaging applications where three-dimensional tissue topography introduces complex optical interactions. Signal uniformity, the complementary component of image fidelity, was examined in Fluorescence Uniformity: Anatomy of Accurate Imaging (Part 1).

The Challenge of Distortion

Image Distortion refers to spatially dependent changes in magnification across the field of view. The two most common types are:

  • Barrel distortion: Objects appear compressed toward the center (like with a fisheye camera)
  • Pincushion distortion: Objects appear stretched away from the center (like with a telephoto lens)

Introducing the Reference Uniformity and Distortion (RUD) Target

QUEL Imaging's Reference Uniformity and Distortion (RUD) target enables the simultaneous characterization of both fluorescence signal uniformity and image distortion.

The RUD target consists of a grid of equally-spaced wells filled with luminescent material embedded within a non-fluorescent, light-absorbing matrix. Illuminating the target with the appropriate excitation wavelength causes fluorescence to be emitted from the wells. By analyzing how this pattern appears in the imaging system, both uniformity and distortion can be quantified precisely.

Characterizing Your System’s Distortion

By comparing the actual positions of fluorescent wells against the imaged pattern, precise distortion metrics can be calculated with a few simple steps:

  1. Using your fluorescent imaging system, take images of the RUD target under typical conditions (working distance, ambient lighting, system settings). Be sure to precisely position the RUD target orthogonal to the imaging axis. 
  2. In the fluorescence image, identify the location of each well. This can be done semi-automatically with the QUEL-QAL Python library. For each well, use QUEL-QAL to calculate the distortion, which is derived from the distance between the actual (imaged) well position and the theoretical well position:
  3. Create a spatial map of distortion.
  4. Plot the distortion versus image height.
Step 1. An example fluorescence image of the RUD target. Step 2. An example of barrel distortion. The actual (imaged) well position (black dots) appear more compressed than the expected position (red dots). Near the center, the dots are aligned and there is no observable distortion.
Step 3. In this example spatial map of distortion, the edges of the field of view are -10% distortion. In other words, if a point is expected to appear 100 pixels from the center, but due to barrel distortion its image shows it is 90 pixels from the center. Step 4. The corresponding distortion versus image height graph.

For detailed step-by-step instructions on using RUD targets, refer to the Use Guide: Uniformity and Distortion Targets.

Best Practices for Development

Understanding and correctly measuring distortion is crucial for developing effective imaging systems. While the analysis may seem complex, focusing on practical considerations will help ensure your system meets clinical needs:

  • How much distortion is tolerable for expected use cases? Consider the precision required when measuring anatomical structures (e.g. tumor margin assessment) may require minimal distortion, while simple presence/absence detection might tolerate more.
  • How will tissue topography effects on image distortion be addressed? Curved or irregular surfaces can appear even more different when image distortion occurs, affecting clinical interpretation.
  • How frequently should system validation be performed? Consider establishing a regular schedule for RUD target assessment to detect any changes in optical performance over time.
  • How will image fidelity be balanced against other system parameters? Consider the trade-offs with sensitivity, depth of field, and system complexity.

Test your system using the same settings (camera exposure time, camera gain, working distance, ambient lighting conditions, etc.) that will be used in clinical practice. If your system has different operating modes, characterize each one separately. By understanding these principles and following proper testing procedures, you'll be better equipped to develop and validate your fluorescence imaging system's distortion.

The Reference Uniformity and Distortion (RUD) target provides crucial insights into fluorescence imaging fidelity, which directly impacts clinical interpretation. For more detailed guidance on system characterization and standardization, refer to the AAPM TG311 guidelines. Implementation tools and reference targets are available to help you meet these standards effectively.

Interested in characterizing your imaging system or developing a customized fluorescence reference target? Contact QUEL Imaging!
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